Method for Characterising a Product by Means of Topological Spectral Analysis

ABSTRACT

The invention relates to a method for generating and optimising a bank of spectral data that can be used in a method for characterising a target product by means of topological spectral analysis based on a limited number of available standards, the method consisting of a first step of performing the same spectral analysis on the standards, and, from the spectra obtained, forming a bank of spectral data A at multiple wavelengths and/or wavelength ranges.

The present invention concerns a method of characterization of a productby topological spectral analysis.

The present invention also concerns the formation of an enlargedspectral database to be used in the improved characterization of aproduct by topological spectral analysis.

More particularly, the present invention concerns a method ofcharacterization of a product by topological spectral analysis in thenear infrared (“NIR”).

The present invention also concerns a device for characterization ofsuch a product by topological spectral analysis.

In particular, the present invention concerns the formation of anenlarged spectral database able to be used for the improvedcharacterization of a product by topological spectral analysis.

The characterization of a product according to the present invention mayconsist in a determination and/or a prediction of any chemical, physicalor physico-chemical characteristic of said product and/or theidentification of a type and/or family of products.

Patent EP0742900 of the applicant constitutes the reference for thefield of topological spectral analysis. It describes a method ofdetermination or prediction of a value Px, a property of a substance Xor a property of a product resulting from a process coming from saidsubstance or the yield of said process, which method consists inmeasuring the absorption D_(i)x of said substance at more than onewavelength in the region of 600 to 2600 nm, comparing the indicatorsignals of these absorptions or their mathematical functions withindicator signals of absorptions Dim at the same wavelengths or theirmathematical functions for a certain number of standards S in a databasefor which one knows said property or yield P, and selecting in thedatabase at least one and preferably at least 2 standards Sm having thesmallest mean values for the absolute values of the difference at eachwavelength i between the signal for the substance and the signal for thestandard Sm in order to obtain the value Px, and taking the mean of saidproperties or yields Pm, when more than one standard Sm is selected.

Although the application of techniques of mathematical analysis tospectral data has been described in patent EP0742900, patent U.S. Pat.No. 6,897,071 also described the integration of the use of NIRtopological analysis and the techniques of the partial least squareswithin a single chemometric approach. In particular, patent U.S. Pat.No. 6,897,071 claims a method for analysis of a substance having anabsorption in the NIR region, comprising: a step of data collection bythe acquisition of a first set of data coming from NIR spectroscopicdata of samples by subjecting the substance to a NIR spectroscopy; astep of data generation by the generating of a second set of data comingfrom NIR spectroscopic data by subjecting the first set of data to apartial least squares regression technique; and an identification stepby the identification of a compound of the substance by means of a NIRtopological analysis of this second data set.

Topological spectral analysis has many advantages compared to theclassical mathematical regression methods. The numerical methodsdescribed for the modeling of the physicochemical properties ofsubstances based on spectral analysis are of a correlation type andinvolve relations of a regression kind between the property (orproperties) being studied. Among such multiple variable analysis onefinds multilinear regression (MLR), principal component regression(PLR), canonical regression and partial least squares (PLS) regression.In all instances, one looks for a relation between the property and thespectrum which can be linear but is usually quadratic or a higheralgebraic form containing regression coefficients applied to eachabsorption. Moreover, the establishing of any regression requires aprogressive calibration, since the approach is empirical and notsupported by any theory.

Thus, WO-A-9207326 describes a method of estimation of property and/orcomposition data of a test sample, consisting in performing a spectralmeasurement on the test sample and estimating the property and/orcomposition data of the test sample from its measured spectrum, based ona predictive model correlating the spectra of the calibration samplewith the property and/or composition data of said calibration samples,in which one performs a determination, based on a checking of themeasured spectrum against the predictive model to find out whether themeasured spectrum is within the range of the calibration sample spectrain the model, and a response is generated if the result of the check isnegative, this response consisting in particular in isolating the testsample, analyzing it with the help of a separate method to determine itsproperty and/or composition data, and updating the predictive model withthis data and with the spectral measurement data obtained.

These techniques have drawbacks, chief of which is the need to establisha strong correlation between the spectrum and the property, and theirdifficulty in handling the positive or negative synergy between thecomponents contributing to this property. For example, to determine thechemical composition of LINA (linear, isoparaffinic, naphthenic,aromatic) in a hydrocarbon feedstock for a catalytic reformer, the useof a PLS technique based on NIR spectra has been described. The modelworks well on the calibration set but the response of the models whenone adds pure hydrocarbons, such as cyclohexane, is not satisfactory,since the model predicts variations in content of isoparaffins andnaphthenes which are the opposite of those experimentally found. What ismore, there are other practical difficulties, primarily due to the needto identify samples of families having the same type of relation betweenthe spectra and the properties being modeled. Thus, the model may belimited, in particular with a nonlinear relation between the spectrumand the property. The precision of the model is reduced especially whenat the limits of the available data. The stability of the model is alsoa problem, as is the need to perform laborious revisions when adding newstandards to obtain the new model, in particular when adjusting to a newfeedstock for a process; thus, the checking of 6 properties for 4products emerging from a distillation unit requires 24 models, each ofwhich needs to be modified for each modification of the feedstock notincluded in the calibration. Another major drawback found with thesetechniques occurs when a point being analyzed is situated outside thepreviously established model; it is then necessary to generate a newdatabase and a new model for each property, which makes this type oftechnique not only little responsive, but also requiring many more hoursof work time.

As for US2010/0211329, this describes a method and a device for thedetermination of the class, the grade and the properties of hydrocarbonsamples—regardless of the ambient temperature, the temperature of theinstrumentation and/or that of the sample—by means of mathematicalcorrelations between the class, the grade and the properties of thehydrocarbons and their spectra—in particular their Raman spectra—from adatabase populated with samples and their measured properties and theirspectra. In particular, US2010/0211329 claims the combined use of roughmathematical models with more refined mathematical models.

It should be noted that topological spectral analysis as such has notreally evolved since the applicant's patent EP0742900. Thus, the presentinvention involves an improvement of said method of topological spectralanalysis. The characteristics of this new method of topological spectralanalysis as well as its advantages will be described in detail in thefollowing specification, as well as the examples, figures and claims.Other goals and advantages of the present invention will appear in thecourse of the following specification, making reference to sampleembodiments given only as illustration and not as a limitation.

The understanding of this specification will be facilitated by regardingthe enclosed FIGS. 1 to 10 in which:

FIG. 1 shows the NIR spectrum of a standard,

FIG. 2 shows a spectral database example A,

FIG. 3 shows a spectral database example B (detection of pollutingwavelengths),

FIG. 4 shows an improved spectral database example A′ (spectral databaseA in which the spectral data corresponding to the polluting wavelengthshave been eliminated),

FIG. 5 shows an enlarged spectral database example E (spectral databaseA or A′ in which intergerms have been added),

FIG. 6 shows an enlarged spectral database example EE (spectral databaseA and/or E in which extragerms have been added),

FIG. 7 shows an enlarged spectral database example EEI (spectraldatabase E and/or EE in which extragerms' have been added),

FIGS. 8 and 9 show, respectively, a graph and a table representingdiscriminant aggregates, and

FIG. 10 shows a spectral database of the type of FIG. 5 in which themeasured characterizations of the standards and calculations of theintergerms have been added.

In particular, all the chemometric approaches of spectral analysis ofthe prior art require the establishing of a spectral database made upfrom a very large initial number of samples and/or standards. Althoughthe prior art cites spectral database formations based on at least 60 orat least 100 samples and/or standards, all the examples describedatabases made up of a distinctly larger number of samples. This numberis even larger in the chemometric approaches using the mathematicalregression methods, whose databases are made up of hundreds or eventhousands of samples and/or standards. The present invention is able toovercome this prior requirement, which opens up a considerable number ofnew applications as shown below.

Thus, first of all, the method according to the present inventionconsists in the preparation of an enlarged spectral database E for alimited number of materials with available standards.

The present invention can be applied advantageously to all type ofspectroscopy, such as but not limited to Raman spectroscopy, RMN, theinfrared range, the UV-visible range, or the near infrared (NIR) range.Preferably, the present invention will be applied to NIR spectroscopy.In fact, NIR spectroscopy presents many advantages as compared to otheranalysis methods, for example, at refineries, petrochemical or chemicallocations, as well as all the fields where the characterization ofchemical products [is done], such as hydrocarbons, and especially fuels,and it can handle a large number of repetitive applications withprecision, speed, and an in-line process. Moreover, the NIR regionbetween 800 and 2500 nm contains all of the molecular information in theform of combinations and harmonics of polyatomic vibrations.

In a first step, one performs a selected type of spectral analysis oneach of the standards and begins to populate the spectral database A byregistering there the spectra (for example, in numerical or digitizedform), preferably the NIR spectra, at several wavelengths (or wavenumbers) for a limited number of materials of available standards.

An example of the formation and representation of this initial spectraldatabase is described by means of FIGS. 1 and 2.

FIG. 1 shows the NIR spectrum of a standard in which one can display asthe spectral quantity the absorbance measured as a function of the wavenumber. Similar spectra are thus likewise established in identicalmanner for each standard. In the present sample representation, ninestandards have been analyzed. Based on these spectra, one draws up atable (spectral database A) a sample representation of which is shown inFIG. 2 for a limited number of wave numbers.

Thus, in the table of FIG. 2 (which thus corresponds to a truncatedview—two parts of the table are shown with different selected wavenumbers), one may notice in the left column the references allowing anidentification of the nine standards and on the first line the value ofthe wave number or ranges of wave numbers; the content of the table thusindicates the values of the spectral quantities (in the present case,the absorbances) corresponding to the pair “standard reference/wavenumber”. These spectral quantities can be every type of signalcharacterizing the spectra, such as the absorbances, transmittances,reflectances, etc.; the absorbances or optical densities being thesignals most commonly used. As an example, we shall also term as signalsthe derivatives of the absorbances or any other measurement resultingfrom another type of mathematical treatment of said absorbances.

The limited number of available standards is generally dictated by theclient and/or the end user, who desire to utilize responsive andreliable methods of inspection while limiting the need to have a largequantity of standards beforehand and having to perform an analysis bythe conventional methods.

One characteristic of the method according to the present invention isthat it thus allows one to overcome the need, dictated by the prior art,to have a very large number of standards. For example, the presentinvention allows one to characterize a sample product based on a numberof available standards less than 100, or even less than 60, or less than50. It has even been possible to obtain very convincing results with thehelp of the present invention based on fewer than 40 availablestandards, or even fewer than 30 or even 20. A minimum of 10 availablestandards is preferred, however, even though the present invention hasalready been used with success with a minimum of 5 available standards.

For the present invention, the following description and the claims, itis obvious to the skilled person that the spectra can be realized as afunction of wavelengths (and/or ranges of wavelengths), since the wavenumber is represented by the inverse of the wave length.

For the present invention, the following description and the claims, thestandards shall also be called “germs” [“0”], the two terms beinginterchangeable.

A second optional step, and preferred according to the presentinvention, then consists in eliminating “polluting” wavelengths andwavelength ranges from the spectral database A. This step involves

-   -   1. repeating at least twice, preferably at least three times,        more preferably at least five times, the same spectral analysis        as the one done during the first step, and this on at least one        of the available standards, preferably on at least two or even        on all of said standards;    -   2. constructing a spectral database B from the measurements done        under point 1 above;    -   3. calculating for each standard selected under point 1 above        and for each wavelength and/or wavelength range (of the spectral        database A) the standard deviations (σ) of the measurements        recorded in database B;    -   4. identifying in database B the wavelengths and/or wavelength        range for which the standard deviation is greater than a        predetermined value;    -   5. eliminating from spectral database A the measurements        corresponding to the wavelengths identified under point 4 above.

Thus, according to one preferred mode of execution of the presentinvention, the use of the second step above allows one to obtain animproved spectral database A′; an example of an improved spectraldatabase A′ is shown in FIG. 4.

An example representing the spectral database B is illustrated in FIG. 3by a table.

One can see here that the same spectral analysis was repeated ten (10)times on the same sample and that the corresponding values of spectralquantities have been listed in the table. The last three rows of thetable correspond respectively and consecutively to

-   -   the mean spectral quantity value VGSmoyenne (“VGSm”) which        corresponds to the sum of the spectral quantity values divided        by the number (“n”) of analyses performed (VGSm=[Σ VGS]/n),        where n=10 in the present representation;    -   the standard deviation (“σ”) which corresponds to the difference        between VGSmax and VGSmin in each column of the table;    -   the ratio (σ/(VGSm/100)) whose value (in percent) is calculated        by dividing the standard deviation by the mean value of the        spectral quantity, and multiplying the result by one hundred.

Thus, the last row of the table lets one identify in the database B thewavelengths and/or wavelength ranges for which the ratio (σ/(VGSm/100))is greater than a predetermined value. According to one mode ofexecution of the present invention, one identifies in table B thecolumns (the wavelengths and/or wavelength ranges) for which the valueof the ratios (σ/(VGSm/100)) is greater than 2% (preferably greater than1.5% or even 1%); next, one eliminates from database A said columns,namely, the values of spectral quantities corresponding to the“polluting” wavelengths. The corresponding columns (that is, those whosewavelength and/or wavelength range are identical) will then beeliminated from spectral database A. It should be noted that in theabove examples tables A and B are representations not having any truerelation with each other; it should also be noted that tables A and Bhave been truncated in order to give a visual representation; inreality, these tables comprise a multitude of columns representing thewavelengths and/or wavelength ranges extracted from the correspondingspectrum, as explained further below in the description.

Thus, one example of the improved spectral database A′ is illustrated inFIG. 4.

An essential characteristic of the method according to the presentinvention consists in that the establishing of the improved spectraldatabase A′ does not need at this stage to make reference [to] and/or[have] the slightest correlation with the chemical and/orphysico-chemical properties of the standards. In fact, this second stepis totally independent.

A third consecutive step of the method according to the presentinvention consists in the actual enlargement of the spectral database A(or the improved spectral database A′). This step consists in generatingsynthetic standards (also known as “intergerms” [“IG”] from theavailable standards and from their values of spectral quantities. Forexample, to generate these IGs one can produce combinations of severalavailable standards of the first step above and populate the spectraldatabase A (or the improved spectral database A′) by means of thesecombinations. These combinations can be made in random fashion or in anoriented fashion as described further on in the text. Said combinationscan consist in any kind of mathematical treatment applied to the valuesof spectral quantities of the standards G. According to one preferredmode of execution of the present invention, said combination consists ina barycenter of the values of spectral quantities (“VGS”) of at leasttwo standards. For example, one could produce these combinations amongtwo, three, or a greater number of available standards at the start,preferably among all the available standards at the start.

An example of a corresponding formula for the generating of a syntheticstandard (IG) from the standards G (to which the VGS correspond) is

[ΣRi×VGSi]/[ΣRi]

where i is a whole number from 1 to the number of standards G chosen forthis combination and R is a real number such that

[ΣRi]>0, and

|[ΣR*i]|/[ΣRi]<0.3, preferably <0.15,

And with R* representing only the negative real numbers.This latter formula can also be described as being the absolute value ofthe sum of the negative real numbers divided by the sum of all the realnumbers.According to a preferred mode of execution of the present invention, atleast one of the Ri is a negative real number (R*).

Proceeding in this way, it is thus possible to enlarge the spectraldatabase A (or the improved spectral database A′) by means of syntheticstandards (also known as “intergerms” or “IG”) and thus obtain anenlarged spectral database E.

According to one preferred mode of execution of the present invention,when the number of standards of the spectral database A (or A′) is “N”,the number of intergerms IG is at least greater than 1.5 N, preferablygreater than 2 N, most preferably greater than 5 N, or even greater than10 N.

On sample representation of the enlarged spectral database E isillustrated in FIG. 5 by a table. One can see here that syntheticstandards (or intergerms “IG”) have been generated by mathematicalcombinations and that the values of corresponding spectral quantitieshave been recorded in the table E. As an example, one can observe intable E (FIG. 5):

-   -   six intergerms “IG” (120022, 120011, 120036, 130038, 13G025 and        13G019;    -   in columns 3 to 5, the germs used to generate each of said        intergerms;    -   in column 2, the weighting applied to the germs selected for the        calculation of the VGS of the intergerms (for example, for the        calculation of the intergerm 12G036, a weighting of (0.44 times        the germ A0000008+0.56 times the germ A0000004) has been        applied).

An essential characteristic of the method according to the presentinvention consists in that the establishment of the enlarged spectraldatabase E has no need at this stage to make reference to and/or theslightest correlation with the chemical and/or physico-chemicalproperties of the standards. In fact, this enlargement step is totallyindependent.

A fourth additional optional and preferred step according to the presentinvention then consists in a supplemental enlargement of the spectraldatabase A or the enlarged spectral database E by means of another typeof synthetic standard which we shall call “extragerms” (“EG”). This stepis particularly relevant when the target product being analyzed containsa plurality of chemical compounds.

It consists of registering in a first sequence the spectral data of atleast one spectrum corresponding to one (or more) of the chemicalcompounds of the target product (also known as “Poles”). Next, in asecond sequence, one proceeds with an additional enlargement of thespectral database by using said Pole(s) and combining them with thegerms “G” (thus, one produces a combination of their values of spectralquantity, VGS).

This second sequence consists in generating synthetic standards (alsoknown as “extragerms” [“EG”]) from the Pole(s) and the availablestandards and their values of spectral quantities. For example, togenerate these EGs one can make combinations of Pole(s) and of severalavailable standards of the first step above and populate the spectraldatabase A and/or E by means of these combinations. These combinationscan be made in random fashion or in an oriented fashion as describedfurther on in the text. Said combinations can consist in any kind ofmathematical treatment applied to the values of spectral quantities ofthe standards G and of the Pole(s). According to one preferred mode ofexecution of the present invention, said combination consists in abarycenter of the values of spectral quantities (“VGS”) of the selectedstandards G and/or the Pole(s). For example, one could produce thesecombinations among at least one Pole and one, two, three, or a greaternumber of available standards at the start, preferably with all thePoles corresponding to all the chemical compounds making up the targetproduct.

An example of a corresponding formula for the generating of a syntheticstandard of type EG from Pole(s) and standards G (to which the VGScorrespond) is

[ΣRi×VGSi+ΣRj×VGSj]/[ΣRi+ΣRj]

where i is a whole number from 1 to the number of standards G chosen forthis combination, j is a whole number from 1 to the number of Pole(s)chosen for this combinationand R is a real number such that

[ΣRi+ΣRj]>0, and

|[ΣR*i]|/[ΣRi+ΣRj]<0.3, preferably <0.15,  (I)

with R* representing only negative real numbers,and preferably each Rj should be such that the ratioRj/[ΣRi+ΣRj] is always between the opposite number of the minimumcontent and the maximum content by weight percentage of the Poles j inthe target product.

The above formula (I) can also be stated as being the absolute value ofthe sum of the negative real numbers “i” divided by the sum of all thereal numbers. According to one preferred mode of execution of thepresent invention, at least one of the Ri is a negative real number(R*).

Proceeding in this way, it is thus possible to enlarge the spectraldatabase A and/or E by means of synthetic standards (“EG”) and thusobtain an enlarged spectral database EE. Optionally, said Poles andtheir VGS can also be integrated in the spectral database EE but thisdoes not constitute a preferred mode of execution according to thepresent invention.

According to one preferred mode of execution according to the presentinvention, when the number of standards of the spectral database A (orA′) is “N” and the number of “Poles” is “M”, the number of extragerms“EG” is at least greater than N×M, preferably greater than 1.5 N×M,preferably greater than 2 N×M.

According to one mode of execution of the present invention, the numberof poles is less than 15, for example less than 10.

According to one mode of execution of the present invention, the numberof poles is less than 0.2 times the number of standards, for example,less than 0.1 times the number of standards.

A sample representation of the enlarged spectral database EE isillustrated in FIG. 6 by the table EE. One can see here that the “Poles”as well as the generation of the synthetic standards “EG” (extragerms)by mathematical combinations and the values of the correspondingspectral quantities have been recorded in the table.

As an example, one can observe in the table EE (FIG. 6):

-   -   six extragerms “EG” (MEG001 to MEG006);    -   in column 2 (“Pole”), the reference of the poles used (for        example, the Pole PAL054 is a particular type of alkylate used        in the composition of gasolines constituting the standards of        the database);    -   in column 3, the reference of the germ used to generate each of        said extragerms;    -   in column 4, the weighting applied to the Poles (X)—the        weighting applied to the germs being thus (1-X). For example, to        calculate the extragerm MEG001, a weighting of (0.15 times the        Pole PAL054+0.85 times the germ A0000009 has been applied).

An essential characteristic of the method according to the presentinvention consists in that the establishment of the enlarged spectraldatabase EE has no need at this stage to make reference to and/or theslightest correlation with the chemical and/or physico-chemicalproperties of the standards. In fact, this enlargement step is totallyindependent.

A fifth additional optional and preferred step according to the presentinvention likewise consists in a supplemental enlargement of theenlarged spectral database E and/or EE by means of another type ofsynthetic standard which we shall call “extragerms′” (“EG′”). This stepis again particularly relevant when the target product being analyzedcontains a plurality of chemical compounds.

It consists of registering in a first sequence the spectral data of atleast one spectrum corresponding to one (or more) of the chemicalcompounds of the target product (also known as “Poles”).

Next, in a second sequence, one proceeds with an additional enlargementof the spectral database E or EE by using said Pole(s) and combiningthem with the intergerms “IG” (combination of their VGS).

This second sequence consists in generating synthetic standards (alsoknown as “extragerms′” [“EG′”]) from the Pole(s) and the “intergerm”standards (“IG”) (and optionally germs “G”) and their values of spectralquantities. For example, to generate these EG's one can makecombinations of Pole(s) and of several intergerms “IG” from the thirdstep above (and optionally germs G from the first step) and populate thespectral database E and/or EE by means of these combinations.

These combinations can be made in random fashion or in an orientedfashion as described further on in the text. Said combinations canconsist in any kind of mathematical treatment applied to the values ofspectral quantities of the synthetic standards (intergerms) “IG” and ofthe Pole(s) (and optionally the germs “G”).

According to one preferred mode of execution of the present invention,said combination consists in a barycenter of the values of spectralquantities (“VGS”) of the intergerms IG and/or the Pole(s) (andoptionally the germs “G”). For example, one could produce thesecombinations among at least one Pole and one, two, three, or a greaternumber of the “IGs” of the third step, preferably with all the “IGs”;and optionally with at least one of the germs “G”, preferably with allof the germs “G”. These combinations will preferably be made with allthe available Poles corresponding to all the chemical compounds makingup the target product.

An example of a corresponding formula for the generating of a syntheticstandard of type EG′ from Pole(s) and synthetic standards IG (to whichthe VGS correspond) is

[ΣRi×VGSi+ΣRj×VGSj+ΣRk×VGSk]/[ΣRi+ΣRj+ΣRk]

where k is a whole number between 1 and the number of syntheticstandards IG chosen for this combination, i is a whole number from 0(preferably 1) to the number of standards G chosen for this combination,j is a whole number from 1 to the number of Pole(s) chosen for thiscombination, and R is a real number such that

[ΣRi+ΣRj+ΣRk]>0, and

|[ΣR*i]+[ΣR*k]|/[ΣRi+ΣRj+ΣRk]<0.3, preferably <0.15,  (II)

with Rk being preferably always positive,with R* representing only the negative real numbers,AND preferably each Rj should be such that the ratioRj/[ΣRi+ΣRj+ΣRk] is always between the opposite number of the minimumcontent and the maximum content in weight percent of the Poles j in thetarget product.

Formula (II) above can also be stated as being the absolute value of thesum of the negative real numbers “i” divided by the sum of all the realnumbers. According to one preferred mode of execution of the presentinvention, at least one of the Ri is a negative real number (R*).

Proceeding in this way, it is thus possible to enlarge the spectraldatabase E and/or EE by means of synthetic standards (“EU”) and thusobtain an enlarged spectral database EEI. Optionally, said Poles andtheir VGS can also be integrated in the spectral database E but thisdoes not constitute a preferred mode of execution according to thepresent invention.

According to one preferred mode of execution of the present invention,when the number of synthetic standards IG of the spectral database E is“Z” and the number of “Poles” is “M”, the number of extragerms' “EG′” isat least greater than Z×M, preferably greater than 1.5 Z×M, preferablygreater than 2 Z×M.

According to another preferred mode of execution of the presentinvention, when the number of synthetic standards IG of the spectraldatabase E is “Z”, the number of germs G is N and the number of “Poles”is “M”, the number of extragerms “EG′” is at least greater than Z×M,preferably greater than 1.5 Z×M, preferably greater than 2 Z×M.

According to one mode of execution of the present invention, the numberof poles is less than 15, for example less than 10.

According to one mode of execution of the present invention, the numberof poles is less than 0.2 times the number of standards, for example,less than 0.1 times the number

A sample representation of the enlarged spectral database EEI isillustrated in FIG. 7 by the table. One can see here the “Poles” as wellas the generation of the synthetic standards “EG′” (extragerms′) bymathematical combinations and that the values of the correspondingspectral quantities have been recorded in the table.

As an example, one can observe in the table EEI (FIG. 7):

-   -   six extragerms' “EG′” (MEP001 to MEP006);    -   in column 5 (“Pole”), the reference of the poles used (for        example, Pole PAL037 is a particular type of alkylate used in        the composition of gasolines making up the standards of the        database);    -   in columns 2 to 4, the reference of the intergerms (combinations        of germs) used to generate each of said extragerms;    -   in column 6, the weighting applied. For example, to calculate        the extragerm MEP004 there has been applied a weighting of [0.9        times an intergerm (corresponding to 0.306 times the germ        A00000061−0.0530 times the germ A0000009+0.647 times the germ        A0000002)+0.1 times the pole PAL037].

An essential characteristic of the method according to the presentinvention consists in that the establishment of the enlarged spectraldatabase EEI has no need at this stage to make reference to and/or theslightest correlation with the chemical and/or physico-chemicalproperties of the standards. In fact, this enlargement step is totallyindependent.

The present invention thus concerns a method of generation andoptimization of a spectral database able to be used in a process ofcharacterization of a target product by topological spectral analysisbased on a limited number of available standards, method consisting, ina first step,

-   -   of performing the same spectral analysis on said standards, and    -   forming from the spectra obtained a spectral database A of        several wavelengths and/or wavelength ranges,        characterized in a second optional step in that one eliminates        from spectral database A the “polluting” wavelengths and/or        wavelength ranges from spectral database A, involving    -   1. repeating at least twice, preferably at least three times,        more preferably at least five times, the same spectral analysis        as the one done during the first step, and this on at least one        of the available standards, preferably on at least two or even        on all of said standards;    -   2. constructing a spectral database B from the measurements done        under point 1 above;    -   3. calculating for each standard selected under point 1 above        and for each wavelength and/or wavelength range (of the spectral        database A) the standard deviations (a) of the measurements        recorded in database B;    -   4. identifying in database B the wavelengths and/or wavelength        range for which the standard deviation is greater than a        predetermined value; and    -   5. eliminating from spectral database A the measurements        corresponding to the wavelengths identified under point 4 above        and thus obtaining an improved spectral database A′,

and also characterized by a third consecutive step which consists in theenlargement of the spectral database A (or the improved spectraldatabase A′), involving making combinations of several standards of thefirst step and populating the spectral database A (or the improvedspectral database A′) by means of these combinations (known as syntheticstandards or intergerms “IG”) and thus obtaining an enlarged spectraldatabase E,

and also characterized by a fourth optional consecutive step whichconsists in the enlargement of the spectral database E, involving afirst sequence of adding to the enlarged spectral database E at leastone spectrum corresponding to at least one (or more) of the chemicalcompounds of the target product (also known as “Poles”) and in a secondsequence making mathematical combinations of said Pole(s) with at leastone standard G of the first step and/or at least one of the standards IGof the third step and populating the spectral database E by means ofthese combinations (known respectively as synthetic extragerm standardsor “EG” or synthetic extragerm′ standards “EG′”) and thus obtaining anenlarged spectral database EE (or EEI).

After having formed the enlarged spectral database according to themethodology developed above, it is possible to use any kind ofconventional mathematical analysis to characterize a sample based on theenlarged spectral database.

According to one preferred mode of execution of the present invention,prior to this characterization an additional intermediate step consistsin then defining an effective method of discrimination able to revealhomogeneous subgroups of products which preferably obey the same typesof spectrum/property relationships thanks to a strong analogy ofmolecular structure.

The methods of discrimination can be based exclusively on techniques ofmathematical analysis (such as factorial analysis and/or analysis byprincipal components). Although some of these mathematical methods mayprove useful, the present invention preferably uses at least one otherempirical step to accomplish this type of discrimination, an empiricalstep based on a visual spectral analysis of the aforementioned standardsand/or poles; even though this does not constitute a preferred mode ofexecution according to the present invention, this visual analysis couldalso be done on the reconstituted spectra (based on their calculatedVGS) of the intergerms and/or extragerms. This empirical step thus makesit possible to reveal minuscule differences between the spectra inquestion, differences which after verification may turn out to besynonymous with the existence of homogeneous subgroups of products, evenif one believed at the outset that the totality of the population ofproducts was homogeneous. This technique of discrimination thus makes itpossible to reveal differences among the products even when the end userwas still unaware of such.

As a reminder, one essential characteristic of the method ofestablishing the aforementioned enlarged spectral database according tothe invention is that it does not have to make reference to and/or havethe least correlation with the chemical and/or physico-chemicalproperties of the standards. According to one preferred mode ofexecution of the present invention, the same is true of thediscrimination step described here.

Thus, according to one mode of execution of the present invention, thediscrimination step then consists in defining, from the enlargedspectral database, aggregates (preferably at least two aggregates),spaces of n dimensions representing the combinations of said aggregates(preferably planes—or two-dimensional spaces—representing pairs ofaggregates), and corresponding spectral boxes.

According to one mode of execution of the present invention, the methodof discrimination also involves at least two particular preferredcharacteristics:

-   -   1. the fact that said method involves an iteration phase during        which one verifies the efficacy of the spectral box and thus the        relevance of the selected aggregates; and    -   2. the fact that the aggregates are built up from at least one        visual analysis of the appearance of the spectra, then allowing        one to construct the equations of the aggregates as a function        of the values of spectral quantities VGS.

The aggregates are thus defined as mathematical functions of the valuesof spectral quantities of the enlarged spectral database making itpossible to regroup and/or discriminate and/or separate families ofproducts within the enlarged spectral database. These aggregates canthus be represented in general by the function Agg=f (VGSi).

According to one preferred mode of execution of the present invention,said function obeys equations

of type

$\frac{\sum\limits_{k = 1}^{n}{\sum\limits_{i = 1}^{p}{{ai}\; {Wi}^{\propto}{Wk}^{\beta}}}}{\sum\limits_{i = 1}^{q}{{ai}\; {Wi}^{\propto}}}$

or preferably of type

$\frac{\sum\limits_{i = 1}^{p}{{ai}\; {Wi}^{\propto}}}{\sum\limits_{i = 1}^{q}{{ai}\; {Wi}^{\propto}}}$

in which

-   -   W represents the values of discriminant spectral quantities VGS,    -   a are positive real numbers,    -   p and q represent the selection of the VGS at the wavelengths        and/or wavelength ranges relevant to the discrimination step,        and    -   ∝ and β are exponents between ⅓ and 3.

As for the iteration phase during which one verifies the efficacy of thespectral box and thus the relevance of the aggregates selected, it isenough to add columns to the previously determined spectral databaserepresenting the equations of the discriminant aggregates, calculate thevalue of said aggregates for each of the standards and/or intergermsand/or extragerms and/or poles of the spectral database, produce thegraphical representations (preferably in spaces of two dimensions foreach pair of aggregates), and thus visualize whether the discriminationhas resulting in revealing homogeneous subgroups of products. Thisdiscrimination step thus makes it possible to divide the spectraldatabase into several (at least two) distinct families (homogeneoussubgroups of products), preferably at least three distinct families.

As an example, FIGS. 8 and 9 represent respectively

-   -   a graph whose abscissa/ordinate axes correspond to two        discriminant aggregates, and    -   a table of corresponding values, whose columns show several        discriminant aggregates of which the first two were used to        produce the graph (FIG. 8).

These figures clearly explain how one succeeds in revealing severalhomogeneous subgroups of products.

Thus, the present invention also concerns a method of characterizationof a product by topological spectral analysis.

The characterization of a product according to the present invention mayinvolve a determination and/or a prediction of any chemical, physical orphysico-chemical property of said product.

According to one mode of execution of the present invention, the firststep is thus characterized by the formation of a spectral database,preferably an enlarged spectral database as described in the presentspecification.

As already indicated above, the graphic representations of the databases(tables) in the appended figures are truncated views, since in realitysaid databases constitute a multitude of columns representing thewavelengths and/or wavelength ranges (or, equivalently, the wave numbersor wave number range) extracted from the corresponding spectra.

According to one mode of execution of the present invention, the numberof wavelengths chosen can be from 2 to 1000, for example, from 5 to 200or from 40 to 80.

The wavelengths chosen can be at regular intervals such as 1 to 50 nm orevery 10 to 50 nm or every 15 to 35 nm or every 1 to 5 nm or any othernanometers; or they can be at irregular intervals, such as intervals of1 to 200 nm, for example 1 to 100 or 1 to 50 in particular from 2 to 50or 4 to 50 or 10 to 60 nm, which can be chosen randomly according to avariation of the shape of the spectral curve at this wavelength, forexample, a peak, a valley or a shoulder, or chosen with chemical orstatistical criteria such as factorial analysis. The wavelengths can bein the region of 600 to 20,000 nm, for example from 625 to 2600 nm, forexample from 800 to 2600 nm, in particular from 1500 to 2600 or from2000 to 2550 nm. The wave numbers can be in the region from 16,600 to500, for example from 16,000 to 3840 cm-1, for example from 12,500 to3840 cm-1 in particular from 6660 to 3840 or from 5000 to 3900 cm-1; thecorresponding frequencies in Hertz can be obtained by multiplying thesewavelengths by 3×10(exp)10 cm/s.

Before being able to determine and/or predict a property of a sample,one must of course measure the values of that property for the standardsand, optionally, for the poles. Thus, according to one mode of executionof the present invention the chemical, physical and/or physico-chemicalproperties of the standards (and optionally of the poles) are determinedby means of conventional analysis techniques. As an example ofconventional analysis techniques but not limited to this, one canmention gas-phase chromatography for chemical compositions. Even thoughit goes without saying that the standards are chosen to cover the rangein which the method is supposed to be used, in one preferred mode ofexecution the present invention allows working with a limited number ofstandards thanks to the aforementioned methodology of enlargement of thespectral database.

Thus, in the present invention, one adds to the spectral database themeasured values of the desired properties for said standards (andoptionally the poles); once the spectral database has been enlarged, onethen calculates the values of said properties for the syntheticintergerm (and optionally extragerm) standards based on the formulaswhich were used to generate these synthetic standards; this calculationis done simply by replacing the values of spectral quantities VGS withthe measured values of said properties of the standards (and optionallythe poles) used in the formulas (and optionally, for the extragerms, bythe values already calculated for the intergerms). One thus ends up witha spectral database formed from a number of points (standards andoptionally intergerms, poles and extragerms) with which the desiredproperties (measured and calculated) are associated. A sample embodiment(truncated view) is shown in FIG. 10.

As an illustration, this is an enlarged spectral database E composed ofstandards (A) and intergerms (IG). The table has been supplemented withcharacteristics of the sought target products, namely, the values RONand MON (the research octane number (RON) and the motor octane number(MON)). These characteristics were thus measured for the standards andcalculated for the intergerms.

In the specification of EP0742900, one then compares the signals, forexample, the absorptions (or their derivatives) for the unknown samplewith signals, for example the absorptions (or their derivatives) at thesame wavelength of the standards, and selects the standards having thesmallest differences. Next, one takes the mean of the properties ofthese selected standards in order to determine the property of theunknown sample. One thus reconstitutes a calculated spectrum of thetarget product to which the characteristic (property) thus calculatedcorresponds.

According to one preferred mode of execution of the present invention,this signal comparison is thus not performed on the entire spectraldatabase but only on a representative portion of the spectral databasefor the homogeneous subgroup to which the sample belongs. This part ofthe spectral database is defined by making preferable use of theaforementioned discrimination method (discriminant aggregates).

Next, one compares the signals, for example the absorptions (or theirderivatives or any other value of a spectral quantity) for the unknownsample (target product) with the same signals and at the same wavelengthof the standards and/or intergerms and/or extragerms and/or polesbelonging to the same homogeneous subgroup, and one selects in thespectral database the standards and/or intergerms and/or extragermsand/or poles having the smallest differences.

Whatever the method used, we shall call hereinafter the points close tothe target product the “close neighbor” points. Next, for example, onecan take the mean of the properties of these standards and/or intergermsand/or extragerms and/or poles selected to determine the soughtcharacteristic (property) of the unknown sample.

According to one particular mode of execution of the present invention,the close neighbors chosen are those having the smallest mean values forthe absolute difference at each wavelength i between the value of thespectral quantity (represented, for example, by the absorbance or aderivative of the latter) Wix for the target product (sample/unknownproduct) and the corresponding signal Wim for the close neighbor. Themeans may refer for example to the mean value of Wix−Wim (regardless ofits sign, i.e., an absolute difference), or of (Wix−Wim)exp2. For eachclose neighbor in the spectral database for the type of product inquestion, one finds the mean difference as described and selects theclose neighbor having the smallest mean differences, namely, at least 1but preferably 2, up to 1000 of the smallest ones, for example, 2 to 100or 2 to 20 but in particular 2 to 10 and especially 2 to 6 of thesmallest ones. This selection of the closest neighbors can be done byany known method, such as one can advantageously use the methodsdescribed in the specification of patent EP0742900 (for example, bydetermining the index of proximity).

According to one particular mode of execution of the present invention,the number of close neighbors can be equal to 1, preferably greater thanor equal to 2, even more preferably greater than or equal to 3.

According to one mode of execution of the present invention, the numberof close neighbors is equal to or less than 50, for example, equal to orless than 20, or even 10.

As indicated above, once the “close neighbor” points have been selected,one can easily take the mean of the properties of these selected closeneighbors (standards and/or intergerms and/or extragerms and/or poles)to determine the property of the unknown sample (the target product).Thus, one reconstitutes a calculated spectrum of the target product towhich the characteristic (property) thus calculated corresponds.

However, and this constitutes a preferred mode of execution of thepresent invention, the applicant has discovered unexpectedly asignificant improvement of the precision and robustness of its methodduring the determination of the sought characteristic (for example, aproperty) of a target product by taking a weighted average of theproperties of these “close neighbor” points (whatever the standardsand/or intergerms and/or extragerms and/or poles), said weighting beinga linear or nonlinear function inversely proportional to the distancebetween the sample (“the target product”) and the “close neighbor”points selected, this weighting being represented for example by theformula

${WEIGHT} = \frac{\frac{1}{{di}^{\; \alpha}}}{\sum\limits_{1}^{n}\frac{1}{{di}^{\; \alpha}}}$

where α is a positive number preferably between 0.5 and 1.5,di is the distance between the target product and the close neighbor i,andn is the total number of close neighbors.

Thus, one will apply a weighting of this type to the properties measured(and optionally calculated) of the “close neighbors” to obtain theproperty of the target product.

One thus reconstitutes a calculated spectrum of the target product towhich the characteristic (property) thus calculated corresponds.

In other words, the calculation of the characteristic Z of the targetproduct is done thanks to the corresponding characteristics Zi of theclose neighbor points, assigning to the characteristics of said closeneighbor points a weight which is larger in said calculation as they arecloser to the target product.

Thus, the present invention also concerns a method of characterizationof a target product involving the following steps:

-   -   1. Formation of a spectral database comprising samples, their        spectra and their measured characteristics (“CAR”, such as the        property “P”),    -   2. Spectral analysis of the target product and comparison of the        spectrum obtained (Spectre PC) with the spectral data of the        database,    -   3. Identification of “close neighbor” points of the target        product, and    -   4. Calculation by topology of the characteristic of the target        product (CARpc/top, for example the property Ppc/top) as a        function of the corresponding characteristics of the close        neighbor points,        characterized in that the calculation of step 4 is based on a        weighting associated with the inverse of the distance between        the target product and the close neighbor points.

One can use the method of the invention to determine more than oneproperty P at a time, for example, at least 2, in particular between 1and 30, for example 2 to 10 properties at a time. Of course, one can usedifferent numbers of standards chosen for each property.

According to another preferred mode of execution of the presentinvention, the applicant has discovered another particularly effectivealternative method.

This method consists in combining one of the aforementioned topologicalmethods of characterization of the target product with any othermathematical model different from the topological methods (preferably aregression model) and enabling a characterization of the target productbased on the values of spectral quantities VGS (for the same property).

Thus, this method involves the preliminary formation of a mathematicalmodel able to calculation the properties of the products as a functionof the values of spectral quantity (VGS) of the database, preferably aregression model (for product characterization based on the previouslyestablished spectral database); this spectral database can be either theaforementioned database A or preferably the database E, EE or EEI, or aselection of said databases. Preferably, this database will be the sameas the one used for the topological method.

This alternative method for characterization of a target productinvolves the following steps:

-   -   1. Formation of a spectral database comprising samples, their        spectra and their measured characteristics (“CAR”, such as the        property “P”),    -   2. Spectral analysis of the target product and comparison of the        spectrum obtained (Spectre PC) with the spectral data of the        database,    -   3. Identification of “close neighbor” points of the target        product, and    -   4. Calculation by topology        -   4.1. of the characteristic of the target product (CARpc/top,            for example the property Ppc/top), and        -   4.2. of its spectrum so calculated (spectre PCcalc),    -   5. Formation from the spectral database of a mathematical model        able to calculate the characteristic of a product based on the        spectral database (CAR/mod, for example property P/mod),    -   6. Calculation of the characterization of the target product PC        by the following formula

CARpc=CARpc/top+[CARpc/mod−CARpccalc/mod]

-   -   where        -   CARpc is the calculated value of the characteristic of the            target product sought,        -   CARpc/top is the value calculated by topology (close            neighbor points) of the characteristic of the target            product,        -   CARpc/mod is the value calculated by the mathematical method            of the characteristic of the target product, and        -   CARpccalc/mod is the value calculated by the mathematical            method of the characteristic of the target product            calculated (by means of the spectral data obtained in point            4.2).

The characterization of a product according to the present invention canthus consist in a determination and/or a prediction of any chemical,physical or physico-chemical characteristic of said product and/or theidentification of a type and/or family of products.

For example, one can determine the presence of individual chemicalcompounds within a composition as well as their concentrations; one canalso determine every kind of property, certain of which are exemplifiedbelow.

Thus, one can use the method for the physico-chemical determination orthe prediction regarding at least one feedstock or a product used in anindustrial oil refining process and/or in petrochemical operations orobtained with the help of the latter. The process can be a hydrocarbonconversion or a separation process, preferably a process of reforming orcatalytic cracking or hydrotreatment or distillation or blending. Inparticular, one can use it to determine at least one property of afeedstock and/or the prediction and/or the determination of at least oneproperty and/or a yield of a product coming from a certain number ofdifferent processes such as processes for separating petroleum productssuch as atmospheric distillation, vacuum distillation or separation bydistillation, under pressure greater than atmospheric pressure, as wellas thermal or catalytic conversion, with or without partial or totalhydrogenation, of a petroleum product, such as catalytic cracking, forexample fluid catalytic cracking (FCC), hydrocracking, reforming,isomerization, selective hydrogenation, viscoreduction or alkylation.

The use of the method in blending operations involving the productionand/or the determination of at least one property of a blend of liquidhydrocarbons (optionally with other additives such as alkyl ethers) isof particular value, whether or not this method involves thedetermination of a blend index for the property in question for eachconstituent of the blend. In this method as applied to the blend, onecan obtain the blend indices simply by calculation and without having toprepare physical blends of standards other than those contained in thedatabase. One can combine in linear or nonlinear fashion the blendindices in the stability ranges in order to determine from the value ofthis combination a value for at least one property of the obtainedblend. One can produce the blend by mixing at least two components fromamong butane, steam-cracked hydrogenated gasoline, isomerate, reformate,MTBE or TAME, and gasoline derived by FCC. One can repeat this processby adding numerically the other constituents separately to the basichydrocarbon liquid to determine a series of blend indices and thendetermine from these indices the properties of the multiconstituentblend.

Examples of properties which can be determined and/or predicted are thefollowing: for automotive fuels/gasolines, at least one among theresearch octane number (RON), the motor octane number (MON) and/or theirarithmetic mean, with or without lead additive and/or content ofmethyl-t-butyl ether or methylisoamyl ether and/or benzene.

For automotive fuels/gasolines, at least one of the vapor tension, thedensity, the volatility, the distillation curve, such as the percentagedistilled at 70° C. and/or at 100° C., the oxygen content or the benzeneor sulfur content, the chemical composition and/or the gum content, forexample expressed in mg/100 ml and/or the lead sensitivity (onedetermines these properties in particular to use in the blendingoperations).

For Diesel fuels or gas oil, at least one of the cetane index (forexample, measurement at the engine), the calculated cetane index, thecloud point, the “discharge point”, the filter ability, the distillationcurve, the density for example at 15° C., the flash point, the viscosityfor example at 40° C., the chemical composition, the sensitivity toadditives and the percentage of sulfur.

For the distillation products coming from crude petroleum, for exampleunder atmospheric pressure, at least one of the density, the sulfurpercentage, the viscosity at 100° C., the distillation curve, theparaffin content, the residual carbon content or the Conradson carboncontent, the naphtha content, the flash point of oil, the cloud pointfor gas oil, such as light gas oil, and/or the viscosity at 100° C.and/or the sulfur content for atmospheric residues and the yield for atleast one of the cuts, gasoline (Bp. 38 to 95° C.), benzene (Bp. 95 to149° C.), naphtha (Bp. 149 to 175 C), kerosene (Bp. 175 to 232° C.),light gas oil (Bp. 232 to 342° C.), heavy gas oil (Bp. 342 to 369° C.)and that of the atmospheric residue above 369° C.

For at least one of a feedstock or a product of a catalytic crackingprocess, such as a FCC process, at least one of the density, the sulfurpercentage, the aniline point, the gas oil index, the gasoline index,the viscosity at 100° C., the index of refraction at 20° C. and/or at60° C., the molecular mass, the distillation temperature, for examplethe distillation temperature at 50%, the aromatic carbon percentage, thetotal nitrogen content and the factors characterizing the crackingcapacity of the feedstock, such as the KUOP, the crackability factor,the cokability factor, and the yield, for example, of gas, of gasoline,of gas oil or of residue. Thus, one can determine the yields and/or theproperties of different products obtained by the distillation of crackedproducts such as RON and/or MON, without anti-knock additive or lead forthe gasoline cut and the viscosity at 100° C. for the distillationresidue.

For at least one of a product or a feedstock of a process of catalyticreforming, at least one of the density, the distillation temperatureand/or the chemical composition (expressed in percent) of straight-chainsaturated hydrocarbons, isoparaffins, naphthenes, aromatic substancesand olefins.

For at least one of a product or a feedstock of a gasoline hydrogenationprocess, at least one of the density, the distillation temperature, RONand/or MON, the vapor tension of gasoline without anti-knock or leadedadditive, the volatility, the chemical composition (expressed inpercentage), of straight-chain saturated hydrocarbons, isoparaffins,naphthenes, aromatic substances such as benzene and themono/di-substituted benzenes, olefins such as cyclical and noncyclicalolefins, diolefins, and the maleic anhydride index.

1. Method of generation and optimization of a spectral database able tobe used in a process of characterization of a target product bytopological spectral analysis based on a limited number of availablestandards, the method consisting of the following steps: in a firststep, of performing the same spectral analysis on said standards, andforming from the spectra obtained a spectral database A of severalwavelengths and/or wavelength ranges, in a second optional step in thatone eliminates from spectral database A the “polluting” wavelengthsand/or wavelength ranges from spectral database A, involving 1.repeating at least twice, preferably at least three times, morepreferably at least five times, the same spectral analysis as the onedone during the first step, and this on at least one of the availablestandards, preferably on at least two or even on all of said standards;2. constructing a spectral database B from the measurements done underpoint 1 above;
 3. calculating for each standard selected under point 1above and for each wavelength and/or wavelength range (of the spectraldatabase A) the standard deviations (a) of the measurements recorded indatabase B;
 4. identifying in database B the wavelengths and/orwavelength range for which the standard deviation is greater than apredetermined value; and
 5. eliminating from spectral database A themeasurements corresponding to the wavelengths identified under point 4above and thus obtaining an improved spectral database A′, and a thirdconsecutive step which consists in the enlargement of the spectraldatabase A (or the improved spectral database A′), involving makingcombinations of several standards of the first step and populating thespectral database A (or the improved spectral database A′) by means ofthese combinations (known as synthetic standards or intergerms “IG”) andthus obtaining an enlarged spectral database E.
 2. Method according toclaim 1 further including a fourth consecutive step consisting in theenlargement of the spectral database E, involving a first sequence ofadding to the enlarged spectral database E at least one spectrumcorresponding to at least one (or more) of the chemical compounds of thetarget product (also known as “Poles”) and in a second sequence makingmathematical combinations of said Pole(s) with at least one standard Gof the first step and/or at least one of the standards IG of the thirdstep and populating the spectral database E by means of thesecombinations (known respectively as synthetic extragerm standards or“EG” or synthetic extragerm′ standards “EG′”) and thus obtaining anenlarged spectral database EE (or EEI).
 3. Method according to claim 1wherein the generation of a synthetic standard (IG) from the standards G(to which the VGS correspond) is done by means of the formula[ΣRi×VGSi]/[ΣRi] where i is a whole number from 1 to the number ofstandards G chosen for this combination and R is a real number such that[ΣRi]>0, and|[ΣR*i]|/[ΣRi]<0.3, preferably <0.15, with R* representing only thenegative real numbers; and preferably at least one of the Ri is anegative real number (R*).
 4. Method according to any one of claim 2wherein the generation of a synthetic standard (EG) from the standards G(to which the VGS correspond) and at least one Pole is done by means ofthe formula [ΣRi×VGSi+ΣRj×VGSj]/[ΣRi+ΣRj] where i is a whole number from1 to the number of standards G chosen for this combination, j is a wholenumber from 1 to the number of Pole(s) chosen for this combination and Ris a real number such that [ΣRi+ΣRj]>0, and|[ΣR*i]|/[ΣRi+ΣRj]<0.3, preferably <0.15,  (I) with R* representing onlynegative real numbers, and preferably each Rj should be such that theratio Rj/[ΣRi+ΣRj] is always between the opposite number of the minimumcontent and the maximum content by weight percentage of the Poles j inthe target product; and preferably at least one of the Ri is a negativereal number (R*).
 5. Method according to any one of claim 2 wherein thegeneration of a synthetic standard (EG′) from the synthetic standardsIG, at least one Pole, and optionally from the standards G is done bymeans of the formula[ΣRi×VGSi+ΣRj×VGSj+ΣRk×VGSk]/[ΣRi+ΣRj+ΣRk] where k is a whole numberbetween 1 and the number of synthetic standards IG chosen for thiscombination, i is a whole number from 0 (preferably 1) to the number ofstandards G chosen for this combination, j is a whole number from 1 tothe number of Pole(s) chosen for this combination, and R is a realnumber such that[ΣRi+ΣRj+ΣRk]>0, and|[ΣR*i]+[ΣR*k]|/[ΣRi+ΣRj+ΣRk]<0.3, preferably <0.15,  (II) with Rk beingpreferably always positive, with R* representing only the negative realnumbers, and preferably each Rj should be such that the ratioRj/[ΣRi+ΣRj+ΣRk] is always between the opposite number of the minimumcontent and the maximum content in weight percent of the Poles j in thetarget product; and preferably at least one of the Ri is a negative realnumber (R*).
 6. Method according to claim 1 wherein the spectralanalysis is of near infrared type.
 7. Method according to claim 1wherein the spectral data is selected from the group consisting of: theabsorbances, transmittances, reflectances or optical densities. 8.Method according to claim 1 wherein the number of available standards tomake the database A is less than 100, especially less than 60 or evenless than
 50. 9. Method according to claim 8 wherein number of availablestandards to make up the database A is less than 40 available standards,especially less than 30 or even less than
 20. 10. Method according toclaim 2 wherein the number of poles is less than 0.2 times the number ofstandards, for example, less than 0.1 times the number of standards. 11.Method according to claim 1 wherein the second step is done byeliminating from the database the polluting wavelengths, namely, thespectral data at wavelengths for which the value of the ratios(σ/(VGSm/100)) is greater than 2% (preferably greater than 1.5% or even1%).
 12. Use of a spectral database obtained by the method according toclaim 1 to characterize a target product.
 13. Use according to claim 12characterized in that the method of characterization of the targetproduct involves the following steps:
 1. Formation of a spectraldatabase comprising samples, their spectra and their measuredcharacteristics (“CAR”, such as the property “P”),
 2. Spectral analysisof the target product and comparison of the spectrum obtained (SpectrePC) with the spectral data of the database,
 3. Identification of “closeneighbor” points of the target product, and
 4. Calculation by topologyof the characteristic of the target product (CARpc/top, for example theproperty Ppc/top) as a function of the corresponding characteristics ofthe close neighbor points, characterized in that the calculation of step4 is based on a weighting associated with the inverse of the distancebetween the target product and the close neighbor points.
 14. Useaccording to claim 12 characterized in that the method ofcharacterization of the target product involves the following steps: 1.Formation of a spectral database comprising samples, their spectra andtheir measured characteristics (“CAR”, such as the property “P”), 2.Spectral analysis of the target product and comparison of the spectrumobtained (Spectre PC) with the spectral data of the database, 3.Identification of “close neighbor” points of the target product, and 4.Calculation by topology 4.1. of the characteristic of the target product(CARpc/top, for example the property Ppc/top), and 4.2. of its spectrumso calculated (spectre PCcalc),
 5. Formation from the spectral databaseof a mathematical model able to calculate the characteristic of aproduct based on the spectral database (CAR/mod, for example propertyP/mod),
 6. Calculation of the characterization of the target product PCby the following formula CARpc=CARpc/top+[CARpc/mod−CARpccalc/mod] whereCARpc is the calculated value of the characteristic of the targetproduct sought, CARpc/top is the value calculated by topology (closeneighbor points) of the characteristic of the target product, CARpc/modis the value calculated by the mathematical method of the characteristicof the target product, and CARpccalc/mod is the value calculated by themathematical method of the characteristic of the target productcalculated (by means of the spectral data obtained in point 4.2).